Comparative Analysis of Static and Dynamic Probabilistic Risk Assessment

This study examines three different methodologies for producing loss-of-mission (LOM) and loss-of-crew (LOC) risks estimates for probabilistic risk assessments (PRA) of crewed spacecraft. The three bottom-up, component-based PRA approaches examined are a traditional static fault tree, a dynamic Monte Carlo simulation, and a fault tree hybrid that incorporates some dynamic elements. These approaches were used to model the reaction control system thruster pod of a generic crewed spacecraft and mission, and a comparative analysis of the methods is presented. The methodologies are assessed in terms of the process of modeling a system, the actionable information produced for the design team, and the overall fidelity of the quantitative risk evaluation generated. The system modeling process is compared in terms of the effort required to generate the initial model, update the model in response to design changes, and support mass-versus-risk trade studies. The results are compared by examining the top-level LOM/LOC estimates and the relative risk driver rankings at the failure mode level. The fidelity of each modeling methodology is discussed in terms of its capability to handle real-world system dynamics such as cold-sparing, changes in mission operations due to loss of redundancy, and common cause failure modes. The paper also discusses the applicability of each methodology to different phases of system development and shows that a single methodology may not be suitable for all of the many purposes of a spacecraft PRA. The fault tree hybrid approach is shown to be best suited to the needs of early assessments during conceptual design phases. As the design begins to mature, the level of detail represented in the risk model must go beyond redundancy and nominal mission operations to include dynamic, time- and state-dependent system responses as well as diverse system capabilities. This is best accomplished using the dynamic simulation approach, since these phenomena are not easily captured by static methods. Ultimately, once the design has been finalized and the goal of the PRA is to provide design validation and requirement verification, more traditional, static fault tree approaches may become as appropriate as the simulation method

Quantitative Performance Evaluation of Uncertainty-Aware Hybrid AADL Designs Using Statistical Model Checking

International audience— Architecture Analysis and Design Language (AADL) is widely used for the architecture design and analysis of safety-critical real-time systems. Based on the Hybrid annex which supports continuous behavior modeling, Hybrid AADL enables seamless interactions between embedded control systems and continuous physical environments. Although Hybrid AADL is promising in dependability prediction through analyzable architecture development, the worst-case performance analysis of Hybrid AADL designs can easily lead to an overly pessimistic estimation. So far, Hybrid AADL cannot be used to accurately quantify and reason the overall performance of complex systems which interact with external uncertain environments intensively. To address this problem, this paper proposes a statistical model checking based framework that can perform quantitative evaluation of uncertainty-aware Hybrid AADL designs against various performance queries. Our approach extends Hybrid AADL to support the modeling of environment uncertainties. Furthermore, we propose a set of transformation rules that can automatically translate AADL designs together with designers' requirements into Networks of Priced Timed Automata (NPTA) and performance queries, respectively. Comprehensive experimental results on the Movement Authority (MA) scenario of Chinese Train Control System Level 3 (CTCS-3) demonstrate the effectiveness of our approach

Automated Hybrid Propulsion Model Construction for Conceptual Aircraft Design and Optimization

Electric and hybrid-electric propulsion systems are key technologies for sustainable aviation. Electric propulsion systems introduce many design possibilities, which must be considered in the conceptual design stage to take full advantage of electrification. This makes for a challenging conceptual design problem. Architecture optimization can be applied to explore large design spaces and automatically find the best architectures for a set of requirements. Electric propulsion architecture optimization requires automated and flexible propulsion system modeling. It also requires the analysis of the propulsion architecture at an aircraft level to compute a meaningful objective function for the optimization. In this study, we present an approach for defining the propulsion system architectures and evaluating their aircraft-level performance. A propulsion architecture is defined using a modular interface, allowing architectures to be automatically evaluated on the aircraft-level for a predefined mission. OpenConcept, an open source conceptual design and optimization toolkit, is used to implement the multidisciplinary problem. We present a case study of the electrification of a regional transport aircraft Beechcraft King Air C90GT with automated definition, integration and evaluation of five different propulsion systems. We perform multidisciplinary design optimization to minimize fuel burn and maximum takeoff weight for a sweep of design ranges and battery specific energies. Our approach opens the door to electric propulsion architecture optimization

A Model-Based System Engineering Approach to Support System Architecting Activities in Early Aircraft Design

The aviation industry aims to reduce its environmental footprint and meet ambitious environmental targets, prompting the exploration of novel aircraft concepts and systems, such as hybrid-electric or distributed propulsion. These emerging technologies introduce complexity to aircraft system architectures, requiring innovative approaches to design, optimization, and safety assessment, particularly for system architecting. Several aspects of system architecting specification and evaluation are typically performed separately, using different people and a mix of manual and model-based processes. Connecting these activities has the potential to make the design process more efficient and effective. This thesis explores how a Model-Based Systems Engineering (MBSE) specification environment can be structured and enriched to enable a better bridge to Multidisciplinary Design Analysis and Optimization (MDAO) and Model-Based Safety Assessment (MBSA) activities. The proposed MBSE approach focuses on enhancing system specifications, particularly for unconventional system architectures, which typically feature greater variability in early design stages. Using the ARCADIA/Capella MBSE environment, a multi-level approach is proposed to structure the system architecture specification and the Property Value Management Tool (PVMT) add-on is used to facilitate the bridge to other system architecting activities. In addition, a catalogue of modeling artifacts is established to facilitate the development of various hybrid-electric system configurations. The MDAO link mechanism is demonstrated with an example from the collaborative AGILE4.0 project. Two test cases demonstrate the implementation of the approach: a hybrid-electric propulsion system and associated sub-systems for the overall approach and the landing gear braking system for the model-based Functional Hazard Analysis (FHA), as an example of an MBSA activity. Overall, this thesis helps improve the integration and collaboration between engineers working on MBSE, MDAO, and MBSA. This better integration will help to reduce the development time and risk. Therefore, the presented thesis contributes to a more efficient aircraft development process, enabling the industry to tackle the emerging needs of unconventional aircraft systems and their integration

Generation of Application Specific Hardware Extensions for Hybrid Architectures: The Development of PIRANHA - A GCC Plugin for High-Level-Synthesis

Architectures combining a field programmable gate array (FPGA) and a general-purpose processor on a single chip became increasingly popular in recent years. On the one hand, such hybrid architectures facilitate the use of application specific hardware accelerators that improve the performance of the software on the host processor. On the other hand, it obliges system designers to handle the whole process of hardware/software co-design. The complexity of this process is still one of the main reasons, that hinders the widespread use of hybrid architectures. Thus, an automated process that aids programmers with the hardware/software partitioning and the generation of application specific accelerators is an important issue. The method presented in this thesis neither requires restrictions of the used high-level-language nor special source code annotations. Usually, this is an entry barrier for programmers without deeper understanding of the underlying hardware platform. This thesis introduces a seamless programming flow that allows generating hardware accelerators for unrestricted, legacy C code. The implementation consists of a GCC plugin that automatically identifies application hot-spots and generates hardware accelerators accordingly. Apart from the accelerator implementation in a hardware description language, the compiler plugin provides the generation of a host processor interfaces and, if necessary, a prototypical integration with the host operating system. An evaluation with typical embedded applications shows general benefits of the approach, but also reveals limiting factors that hamper possible performance improvements

Low-Overhead Migration of Read-Only and Read-Mostly Data for Adapting Applications to Hybrid Memory Systems

Memory systems containing different types of memory with varying capacity, latency, and bandwidth are rapidly becoming mainstream. Conventional memory management techniques do not suffice for these systems; they require alternative strategies to appropriately and effectively adapt application memory placement to these heterogeneous memory tiers. Software-based placement and movement strategies are the most desirable due to their flexibility and ease of adoption by end-users. However, there are substantial sources of overhead present when synchronizing low-level data movement with the operating system and running applications.This thesis proposes a novel method of reducing these memory movement overheads on hybrid memory systems. Many data objects are only written to early in their life cycle (i.e. shortly after allocation) and are effectively read-only after these initial writes. If this read-only and read-mostly data is duplicated across memory tiers, as opposed to moved, the application, in many cases, is able to avoid certain types of transfer overhead, such as page table entry (PTE) and MMU cache (TLB) synchronization stalls.This work describes the design and implementation of a kernel module, mtier that implements this optimization on memory that has been explicitly marked as read-only. Our evaluation demonstrates that this approach has the potential to substantially reduce data movement overheads, especially in applications that are multi-threaded and require frequent movement of data, allowing a flexible, software based approach for memory management in hybrid systems

Distributed Hybrid Simulation of the Internet of Things and Smart Territories

This paper deals with the use of hybrid simulation to build and compose heterogeneous simulation scenarios that can be proficiently exploited to model and represent the Internet of Things (IoT). Hybrid simulation is a methodology that combines multiple modalities of modeling/simulation. Complex scenarios are decomposed into simpler ones, each one being simulated through a specific simulation strategy. All these simulation building blocks are then synchronized and coordinated. This simulation methodology is an ideal one to represent IoT setups, which are usually very demanding, due to the heterogeneity of possible scenarios arising from the massive deployment of an enormous amount of sensors and devices. We present a use case concerned with the distributed simulation of smart territories, a novel view of decentralized geographical spaces that, thanks to the use of IoT, builds ICT services to manage resources in a way that is sustainable and not harmful to the environment. Three different simulation models are combined together, namely, an adaptive agent-based parallel and distributed simulator, an OMNeT++ based discrete event simulator and a script-language simulator based on MATLAB. Results from a performance analysis confirm the viability of using hybrid simulation to model complex IoT scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0487